A quarterly newsletter from the Botany Dept (NMNH) and the U.S. National Herbarium.

October 2018

10/31/2018

Stuart Davies, David Kenfack, Lauren Krizel, Mauro Lepore, and Gabriel Arellano traveled to Nové Hrady, Czechia for the 8th Annual ForestGEO Analytical Workshop from 19 July to 3 August. The workshop provided an opportunity to bring global participants together to foster research and scientific collaborations. Participants also embarked on two field-site visits to the ForestGEO Zofin plot located in the Novohradské Mountains.

On 28 September, Gary Krupnick participated in a panel discussion after the showing of the film, Seed: The Untold Story, during a “film-talkback” series of the Rappahannock Association for Arts and Community (RAAC). In partnership with the Smithsonian Conservation Biology Institute and the American Conservation Film Festival, RAAC hosted a special Farm Tour Edition of the talkback series featuring farming-related films over two nights. Seed: The Untold Story follows passionate seed keepers protecting a 12,000 year-old food legacy. In the last century, 94 percent of our seed varieties have disappeared. As biotech chemical companies control the majority of our seeds, farmers, scientists, lawyers, and indigenous seed keepers fight a David and Goliath battle to defend the future of our food. The film showing and discussion took place at the Little Washington Theater in Washington, Virginia.

Alice Tangerini served as one of the committee organizers and as a participant in the Guild of Natural Science Illustrators 2018 conference held in Washington, DC from 14-21 July. The conference marked the 50th anniversary of the formation of the Guild, which began at NMNH with a core group of local and NMNH staff illustrators. This year’s conference was held mainly at American University with a day at the NMNH featuring behind-the-scene tours of the research departments, workshops led by Guild members in the Q?rius education center, and talks in Baird Auditorium. Tangerini and volunteer Mary Monsma chaired the Committees on signage and way-finding and catering. Tours of the Botany Department were led by Leslie Brothers and Meghann Toner, with Tangerini providing a final stop at her office. An exhibit of Guild work was displayed at the AAAS Building, for which Tangerni provided a selection of historic artworks from the Botanical Art Collection.

10/30/2018

On 2 September 2018, a devastating fire destroyed the 200-year-old National Museum of Brazil (Museu Nacional) in Rio de Janeiro. The National Museum of Natural History (NMNH) has a long history of collaboration with Brazilian scientists and many of them are presently working at the Smithsonian. Reprinted below is a public statement that Kirk Johnson, Sant Director of NMNH, released in partnership with the directors of 11 other large natural museums. An effort is underway to collect digital documentation of the Museum’s collections and the galleries (images, 3D models, digital records). Researchers and visitors can send their information to any of the following email addresses: thg.museo@gmail.com, lusantosmuseo@gmail.com, and isabelasfrreitas@gmail.com.

On Sunday evening, a massive fire devastated the National Museum of Brazil. Founded 200 years ago, the museum is Brazil's oldest scientific institution and one of the largest and most renowned museums in Latin America, with a collection of 20 million artifacts and specimens.

It is with a profound sense of loss that our museums share our condolences with our colleagues in Brazil and the public they serve. The importance of the collections lost during this tragic event cannot be overstated. The National Museum is home to priceless artifacts and specimens that hold incalculable value to science—from major pieces of Brazil’s scientific and cultural heritage, to the historic building itself, this is a loss not only for Brazil but for the world.

Times like these are a sobering reminder that natural history matters. Natural history museums document, protect, and celebrate the natural world. Our collections are an invaluable library of moments of life on Earth—each artifact and specimen is a crucial record of how the world became what it is today and a clue into how we can protect it in the future.

While we can’t change the events of this weekend, we as natural history museums remain committed to working together to use our collections and collective scientific knowledge to generate and safeguard information that can be used by the worldwide community. As our colleagues in Brazil look to the future, we commit to supporting them in the coming weeks, months, and years.

10/25/2018

For many biologists, a core joy of our work is observing the natural world, formulating questions about our observations, and testing our ideas using clever experiments. Days are spent exploring museum cabinets or mountain valleys – few of us dream up romantic visions of deskwork at the computer. Computational research, however, is no doubt on the rise in biology, fueled by shrinking infrastructure costs, rapid computational advances, and a growing literacy among biologists in the relevant programming tools.

With more and more specimens being digitized, deep learning models can one day ask questions about quantitative traits such as leaf shape. (photo courtesy of NMNH)

Two rapidly growing fields of ecology and evolution – (phylo)genomics and niche modeling – have already revealed the utility of computational tools when applied to biological questions, with far reaching implications for both basic research and conservation planning, particularly for plants. Yet the well of computational resources has just barely been tapped. For one, genomics and niche modeling take advantage of stereotypical data sources for studying ecology and evolution – sequence data and presence-absence data – but there are many non-traditional sources of data that computers are now poised to ingest and analyze with astonishing speed. These include complex data that machines are well suited to organize for analysis, such as continuous signals (light, audio, chemical, etc.), meshes from 3D scans, and images, whether microscopic, satellite, or traditional. And speed? Consider the latest collaboration between Berkeley National Laboratory, Oak Ridge National Laboratory, and NVIDIA (Kurth et al. 2018, arXiv:1810.01993), in which Kurth et al. showcased a machine learning model operating at 1.13 exaflops (1018 calculations per second). Their model was trained in under 3 hours to detect the pixel-level presence of tropical storms in exceptionally large (3.5 TB) atmospheric datasets. Such “deep learning” models hold promise for studying biological questions, but their development in the context of ecology and evolution is still in its early days.

Deep learning defines a class of computational models that are characterized by linking together a hierarchical chain of data transformations and calculations (i.e., fairly simple matrix algebra) to probabilistically “learn” features of a given dataset. Often, these models are used to supply predictions for a related dataset based on common features (this is called supervised learning and is the most common deep learning approach). One can easily visualize the steps involved in a simple deep learning model using Excel – indeed the most complex component is the learning process, where “known” datasets are passed through the model and thousands of randomly initialized parameters are updated iteratively to improve the model predictions. Primary layers of the models learn rough scale representations of the data where deeper layers learn fine scale differences between the features of interest.

One of the most common applications of deep learning is for object detection in images, where a labeled dataset of images is used to train a model that subsequently is used to predict the labels of unknown images. In 2012, the state-of-the-art image model was able to detect the differences between images containing dogs and those containing cats with an accuracy of approximately 80%. With the proper know-how, one can now build and train a dogs vs. cats model with near perfect accuracy in a matter of minutes.

With these rapid advances in mind, the goal of my postdoctoral research is to develop these methods for use in ecology and evolution and apply them to better understand patterns of biodiversity hidden within complex and noisy datasets. For example, colleagues and I at the University of Chicago recently developed a machine-learning model to evaluate ecological assemblages and their (phylogenetic, functional, and taxonomic) structure. Using this model, we can integrate local community surveys, phylogenetic trees, and biogeographic scale data to quantitatively characterize the regional distribution of biotas and their contributions to individual local assemblages. Unlike the examples I provided above, this model is unsupervised (meaning there are no known labels) and makes use of biogeographic data as images to learn the spatial relationships between species in order to generate characteristic biotas. We are currently using this approach to examine the relationship between bird and plant communities across the forest patches in the Himalayas, and we are finding strong concomitance between the structural patterns in both groups. While we are now working to examine the biological basis of this association with more fieldwork, it was the model itself that was the primary heuristic we used to detect this pattern (the methods are available as an R package called ecostructure).